Overview

Dataset statistics

Number of variables20
Number of observations25408
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 MiB
Average record size in memory222.9 B

Variable types

DateTime1
Numeric16
Categorical3

Alerts

CIU is highly imbalanced (58.0%)Imbalance
flag is highly imbalanced (83.7%)Imbalance
class is highly imbalanced (69.7%)Imbalance
KPMM is highly skewed (γ1 = 156.2735774)Skewed
ROA is highly skewed (γ1 = -24.03264718)Skewed
BOPO is highly skewed (γ1 = 121.6472499)Skewed
CR is highly skewed (γ1 = 34.53329857)Skewed
NNPL is highly skewed (γ1 = 55.98854843)Skewed
MA_KPMM is highly skewed (γ1 = 110.2057154)Skewed
MA_BOPO is highly skewed (γ1 = 83.13429603)Skewed
D_CR is highly skewed (γ1 = 23.51700556)Skewed
MA_CR is highly skewed (γ1 = 25.35747354)Skewed
MA_NNPL is highly skewed (γ1 = 42.33558808)Skewed
NNPL has 420 (1.7%) zerosZeros
D_KPMM has 491 (1.9%) zerosZeros
D_ROA has 674 (2.7%) zerosZeros
D_BOPO has 750 (3.0%) zerosZeros
D_CR has 702 (2.8%) zerosZeros
D_NNPL has 841 (3.3%) zerosZeros
MA_NNPL has 314 (1.2%) zerosZeros

Reproduction

Analysis started2024-05-09 03:03:30.943582
Analysis finished2024-05-09 03:03:42.350617
Duration11.41 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct55
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size397.0 KiB
Minimum2010-09-30 00:00:00
Maximum2023-12-31 00:00:00
2024-05-09T10:03:42.387761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:42.441377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SandiBPR
Real number (ℝ)

Distinct1763
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean603207.89
Minimum600001
Maximum620195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size397.0 KiB
2024-05-09T10:03:42.494039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum600001
5-th percentile600095
Q1600858
median601351
Q3602037
95-th percentile620100
Maximum620195
Range20194
Interquartile range (IQR)1179

Descriptive statistics

Standard deviation5673.1333
Coefficient of variation (CV)0.0094049388
Kurtosis4.86973
Mean603207.89
Median Absolute Deviation (MAD)587
Skewness2.5875371
Sum1.5326306 × 1010
Variance32184442
MonotonicityIncreasing
2024-05-09T10:03:42.550961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
601707 42
 
0.2%
601017 32
 
0.1%
620033 27
 
0.1%
600861 25
 
0.1%
601058 24
 
0.1%
602702 24
 
0.1%
601298 23
 
0.1%
600797 23
 
0.1%
600551 23
 
0.1%
601690 23
 
0.1%
Other values (1753) 25142
99.0%
ValueCountFrequency (%)
600001 15
0.1%
600002 15
0.1%
600003 15
0.1%
600004 15
0.1%
600005 15
0.1%
600007 15
0.1%
600008 15
0.1%
600009 15
0.1%
600010 15
0.1%
600011 15
0.1%
ValueCountFrequency (%)
620195 2
 
< 0.1%
620194 1
 
< 0.1%
620193 2
 
< 0.1%
620192 2
 
< 0.1%
620191 4
< 0.1%
620190 4
< 0.1%
620189 4
< 0.1%
620188 7
< 0.1%
620187 8
< 0.1%
620186 8
< 0.1%

KPMM
Real number (ℝ)

SKEWED 

Distinct9347
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.58881
Minimum-5354
Maximum600619
Zeros6
Zeros (%)< 0.1%
Negative227
Negative (%)0.9%
Memory size397.0 KiB
2024-05-09T10:03:42.605587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-5354
5-th percentile13.4635
Q123.59
median35.48
Q357.3125
95-th percentile120.7765
Maximum600619
Range605973
Interquartile range (IQR)33.7225

Descriptive statistics

Standard deviation3794.243
Coefficient of variation (CV)49.540436
Kurtosis24707.349
Mean76.58881
Median Absolute Deviation (MAD)14.53
Skewness156.27358
Sum1945968.5
Variance14396280
MonotonicityNot monotonic
2024-05-09T10:03:42.657610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 86
 
0.3%
30 24
 
0.1%
14 23
 
0.1%
12 19
 
0.1%
24 18
 
0.1%
16 18
 
0.1%
26.63 17
 
0.1%
22 16
 
0.1%
18 15
 
0.1%
19.66 14
 
0.1%
Other values (9337) 25158
99.0%
ValueCountFrequency (%)
-5354 1
< 0.1%
-1801.98 1
< 0.1%
-896.82 1
< 0.1%
-623.76 1
< 0.1%
-604.55 1
< 0.1%
-537.02 1
< 0.1%
-401.75 2
< 0.1%
-392.06 1
< 0.1%
-377.56 1
< 0.1%
-337.49 1
< 0.1%
ValueCountFrequency (%)
600619 1
< 0.1%
63519 1
< 0.1%
25529 1
< 0.1%
10330 1
< 0.1%
9027.2 1
< 0.1%
6371.05 1
< 0.1%
4310.03 1
< 0.1%
4286 1
< 0.1%
2896 1
< 0.1%
2770 1
< 0.1%

ROA
Real number (ℝ)

SKEWED 

Distinct2924
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1275448
Minimum-999.99
Maximum489
Zeros223
Zeros (%)0.9%
Negative5003
Negative (%)19.7%
Memory size397.0 KiB
2024-05-09T10:03:42.708707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-999.99
5-th percentile-5.9065
Q10.29
median1.8
Q33.52
95-th percentile7.51
Maximum489
Range1488.99
Interquartile range (IQR)3.23

Descriptive statistics

Standard deviation16.090609
Coefficient of variation (CV)14.270483
Kurtosis1295.6612
Mean1.1275448
Median Absolute Deviation (MAD)1.59
Skewness-24.032647
Sum28648.658
Variance258.90769
MonotonicityNot monotonic
2024-05-09T10:03:42.762684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 223
 
0.9%
2 69
 
0.3%
1.34 64
 
0.3%
2.32 63
 
0.2%
1.95 57
 
0.2%
1.53 56
 
0.2%
0.78 56
 
0.2%
0.29 56
 
0.2%
1 56
 
0.2%
1.8 56
 
0.2%
Other values (2914) 24652
97.0%
ValueCountFrequency (%)
-999.99 1
< 0.1%
-777.62 1
< 0.1%
-733.67 1
< 0.1%
-571 1
< 0.1%
-567 1
< 0.1%
-531.3 1
< 0.1%
-475.44 1
< 0.1%
-459 1
< 0.1%
-329.59 1
< 0.1%
-306.81 1
< 0.1%
ValueCountFrequency (%)
489 1
< 0.1%
339 1
< 0.1%
302 1
< 0.1%
299 1
< 0.1%
283 1
< 0.1%
259 1
< 0.1%
243 1
< 0.1%
220 1
< 0.1%
162.98 1
< 0.1%
157.96 1
< 0.1%

BOPO
Real number (ℝ)

SKEWED 

Distinct8083
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.80987
Minimum-18511
Maximum80611.14
Zeros27
Zeros (%)0.1%
Negative14
Negative (%)0.1%
Memory size397.0 KiB
2024-05-09T10:03:42.817360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-18511
5-th percentile62.5505
Q178.1075
median87.03
Q397.51
95-th percentile152.8595
Maximum80611.14
Range99122.14
Interquartile range (IQR)19.4025

Descriptive statistics

Standard deviation552.56974
Coefficient of variation (CV)5.3746758
Kurtosis17809.871
Mean102.80987
Median Absolute Deviation (MAD)9.56
Skewness121.64725
Sum2612193.3
Variance305333.31
MonotonicityNot monotonic
2024-05-09T10:03:42.870184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.01 80
 
0.3%
100 33
 
0.1%
0 27
 
0.1%
84.84 25
 
0.1%
83.51 22
 
0.1%
83.58 21
 
0.1%
266.3 20
 
0.1%
82.73 20
 
0.1%
99.46 18
 
0.1%
77.88 18
 
0.1%
Other values (8073) 25124
98.9%
ValueCountFrequency (%)
-18511 1
< 0.1%
-3276 2
< 0.1%
-3092 2
< 0.1%
-2558 1
< 0.1%
-926.96 1
< 0.1%
-525.87 1
< 0.1%
-390.85 1
< 0.1%
-261.13 1
< 0.1%
-220.61 1
< 0.1%
-18 1
< 0.1%
ValueCountFrequency (%)
80611.14 1
< 0.1%
9194 1
< 0.1%
8987 1
< 0.1%
8910 1
< 0.1%
8827 1
< 0.1%
8787 1
< 0.1%
8773 1
< 0.1%
8584 1
< 0.1%
8177 1
< 0.1%
7378 1
< 0.1%

CR
Real number (ℝ)

SKEWED 

Distinct6728
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.249993
Minimum-0.6
Maximum20385
Zeros47
Zeros (%)0.2%
Negative1
Negative (%)< 0.1%
Memory size397.0 KiB
2024-05-09T10:03:42.922170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.6
5-th percentile7.27
Q113.15
median20.18
Q332.01
95-th percentile77.179
Maximum20385
Range20385.6
Interquartile range (IQR)18.86

Descriptive statistics

Standard deviation268.09743
Coefficient of variation (CV)6.3455023
Kurtosis1768.2712
Mean42.249993
Median Absolute Deviation (MAD)8.4
Skewness34.533299
Sum1073487.8
Variance71876.23
MonotonicityNot monotonic
2024-05-09T10:03:42.977538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.74 87
 
0.3%
0 47
 
0.2%
20.91 32
 
0.1%
11.6 25
 
0.1%
10.48 23
 
0.1%
19.19 23
 
0.1%
8.13 23
 
0.1%
16.46 22
 
0.1%
15.47 22
 
0.1%
15 22
 
0.1%
Other values (6718) 25082
98.7%
ValueCountFrequency (%)
-0.6 1
 
< 0.1%
0 47
0.2%
0.0048 1
 
< 0.1%
0.0087 1
 
< 0.1%
0.0149 1
 
< 0.1%
0.029 1
 
< 0.1%
0.0568 1
 
< 0.1%
0.0633 1
 
< 0.1%
0.0646 1
 
< 0.1%
0.0675 1
 
< 0.1%
ValueCountFrequency (%)
20385 1
< 0.1%
9999.99 1
< 0.1%
9960.68 1
< 0.1%
8296.72 1
< 0.1%
8145.45 1
< 0.1%
7488.88 1
< 0.1%
7347.7 1
< 0.1%
7263.84 1
< 0.1%
7140.32 1
< 0.1%
7020.12 1
< 0.1%

NNPL
Real number (ℝ)

SKEWED  ZEROS 

Distinct3557
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.024635
Minimum-0.5
Maximum3028
Zeros420
Zeros (%)1.7%
Negative1
Negative (%)< 0.1%
Memory size397.0 KiB
2024-05-09T10:03:43.031152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile0.63
Q13.21
median6.58
Q312.57
95-th percentile26.91
Maximum3028
Range3028.5
Interquartile range (IQR)9.36

Descriptive statistics

Standard deviation32.678116
Coefficient of variation (CV)3.2597811
Kurtosis3966.6348
Mean10.024635
Median Absolute Deviation (MAD)4.1
Skewness55.988548
Sum254705.93
Variance1067.8593
MonotonicityNot monotonic
2024-05-09T10:03:43.083064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 420
 
1.7%
0.01 43
 
0.2%
3.82 43
 
0.2%
2.49 36
 
0.1%
3.9 35
 
0.1%
3.5 35
 
0.1%
1 35
 
0.1%
3.2 34
 
0.1%
4.82 34
 
0.1%
3.96 34
 
0.1%
Other values (3547) 24659
97.1%
ValueCountFrequency (%)
-0.5 1
 
< 0.1%
0 420
1.7%
0.01 43
 
0.2%
0.02 9
 
< 0.1%
0.03 20
 
0.1%
0.04 6
 
< 0.1%
0.05 10
 
< 0.1%
0.06 13
 
0.1%
0.07 10
 
< 0.1%
0.08 15
 
0.1%
ValueCountFrequency (%)
3028 1
 
< 0.1%
1662 1
 
< 0.1%
1559 1
 
< 0.1%
1557 1
 
< 0.1%
1499 1
 
< 0.1%
1256 2
 
< 0.1%
1165 1
 
< 0.1%
1127 1
 
< 0.1%
311.9 2
 
< 0.1%
100 12
< 0.1%

D_KPMM
Real number (ℝ)

ZEROS 

Distinct8689
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0077549
Minimum-600554.36
Maximum600556.5
Zeros491
Zeros (%)1.9%
Negative12484
Negative (%)49.1%
Memory size397.0 KiB
2024-05-09T10:03:43.135556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-600554.36
5-th percentile-11.9465
Q1-2.17
median0
Q32.05
95-th percentile13.0565
Maximum600556.5
Range1201110.9
Interquartile range (IQR)4.22

Descriptive statistics

Standard deviation5355.7254
Coefficient of variation (CV)5314.512
Kurtosis12446.702
Mean1.0077549
Median Absolute Deviation (MAD)2.11
Skewness0.028090917
Sum25605.036
Variance28683795
MonotonicityNot monotonic
2024-05-09T10:03:43.189756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 491
 
1.9%
1 44
 
0.2%
0.25 42
 
0.2%
0.39 41
 
0.2%
-0.07 40
 
0.2%
0.18 40
 
0.2%
0.43 38
 
0.1%
-0.14 37
 
0.1%
-0.32 37
 
0.1%
0.32 36
 
0.1%
Other values (8679) 24562
96.7%
ValueCountFrequency (%)
-600554.36 1
< 0.1%
-53189 1
< 0.1%
-5869.71 1
< 0.1%
-5364.13 1
< 0.1%
-4717.17 1
< 0.1%
-4243.52 1
< 0.1%
-3143.49 1
< 0.1%
-2742.26 1
< 0.1%
-2621.54 1
< 0.1%
-2470.24 1
< 0.1%
ValueCountFrequency (%)
600556.5 1
< 0.1%
63506.09 1
< 0.1%
15199 1
< 0.1%
8257.45 1
< 0.1%
5290.99 1
< 0.1%
5204.51 1
< 0.1%
4246.09 1
< 0.1%
2864.71 1
< 0.1%
2675.26 1
< 0.1%
2623.24 1
< 0.1%

MA_KPMM
Real number (ℝ)

SKEWED 

Distinct15694
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.084933
Minimum-2708.505
Maximum300341.82
Zeros2
Zeros (%)< 0.1%
Negative198
Negative (%)0.8%
Memory size397.0 KiB
2024-05-09T10:03:43.240453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2708.505
5-th percentile13.85
Q123.695
median35.5075
Q357.14
95-th percentile119.6015
Maximum300341.82
Range303050.33
Interquartile range (IQR)33.445

Descriptive statistics

Standard deviation2685.6084
Coefficient of variation (CV)35.297507
Kurtosis12302.73
Mean76.084933
Median Absolute Deviation (MAD)14.3975
Skewness110.20572
Sum1933166
Variance7212492.7
MonotonicityNot monotonic
2024-05-09T10:03:43.293530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 61
 
0.2%
14 14
 
0.1%
24.21 13
 
0.1%
12 12
 
< 0.1%
30 11
 
< 0.1%
58.87 11
 
< 0.1%
22.195 10
 
< 0.1%
16 9
 
< 0.1%
30.155 9
 
< 0.1%
26.63 9
 
< 0.1%
Other values (15684) 25249
99.4%
ValueCountFrequency (%)
-2708.505 1
< 0.1%
-2671.935 1
< 0.1%
-1349.4 1
< 0.1%
-453.485 1
< 0.1%
-433.89 1
< 0.1%
-415.56 1
< 0.1%
-401.75 1
< 0.1%
-396.905 1
< 0.1%
-364.845 1
< 0.1%
-312.31 1
< 0.1%
ValueCountFrequency (%)
300341.82 1
< 0.1%
300340.75 1
< 0.1%
36924.5 1
< 0.1%
31765.955 1
< 0.1%
17929.5 1
< 0.1%
6668.615 1
< 0.1%
4898.475 1
< 0.1%
3768.795 1
< 0.1%
3436.195 1
< 0.1%
2810 1
< 0.1%

D_ROA
Real number (ℝ)

ZEROS 

Distinct4706
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.18432071
Minimum-999.99
Maximum997.13
Zeros674
Zeros (%)2.7%
Negative12726
Negative (%)50.1%
Memory size397.0 KiB
2024-05-09T10:03:43.346700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-999.99
5-th percentile-3.35
Q1-0.55
median-0.01
Q30.48
95-th percentile2.66
Maximum997.13
Range1997.12
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation18.2192
Coefficient of variation (CV)-98.845109
Kurtosis1372.3834
Mean-0.18432071
Median Absolute Deviation (MAD)0.51
Skewness-2.8138872
Sum-4683.2205
Variance331.93926
MonotonicityNot monotonic
2024-05-09T10:03:43.401638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 674
 
2.7%
-0.04 135
 
0.5%
0.04 119
 
0.5%
-0.02 117
 
0.5%
0.02 115
 
0.5%
-0.25 113
 
0.4%
0.21 95
 
0.4%
-0.08 94
 
0.4%
0.08 92
 
0.4%
-0.27 91
 
0.4%
Other values (4696) 23763
93.5%
ValueCountFrequency (%)
-999.99 1
< 0.1%
-865.33 1
< 0.1%
-762.64 1
< 0.1%
-530.8 1
< 0.1%
-489.2 1
< 0.1%
-441.8 1
< 0.1%
-429.02 1
< 0.1%
-296 1
< 0.1%
-290.55 1
< 0.1%
-279.1 1
< 0.1%
ValueCountFrequency (%)
997.13 1
< 0.1%
779.46 1
< 0.1%
539.35 1
< 0.1%
504.9 1
< 0.1%
502.28 1
< 0.1%
483.78 1
< 0.1%
418.8 1
< 0.1%
336 1
< 0.1%
272.81 1
< 0.1%
259.28 1
< 0.1%

MA_ROA
Real number (ℝ)

Distinct7388
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2197052
Minimum-604.555
Maximum395.5
Zeros122
Zeros (%)0.5%
Negative5112
Negative (%)20.1%
Memory size397.0 KiB
2024-05-09T10:03:43.566932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-604.555
5-th percentile-5.73325
Q10.35
median1.825
Q33.505
95-th percentile7.55825
Maximum395.5
Range1000.055
Interquartile range (IQR)3.155

Descriptive statistics

Standard deviation12.830518
Coefficient of variation (CV)10.51936
Kurtosis649.372
Mean1.2197052
Median Absolute Deviation (MAD)1.56
Skewness-13.944823
Sum30990.269
Variance164.6222
MonotonicityNot monotonic
2024-05-09T10:03:43.619041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 122
 
0.5%
2 36
 
0.1%
1.69 32
 
0.1%
1.29 32
 
0.1%
2.12 29
 
0.1%
2.4 28
 
0.1%
0.65 28
 
0.1%
2.335 28
 
0.1%
2.835 27
 
0.1%
1.35 27
 
0.1%
Other values (7378) 25019
98.5%
ValueCountFrequency (%)
-604.555 1
< 0.1%
-501.425 1
< 0.1%
-499.995 1
< 0.1%
-396.3 1
< 0.1%
-387.89 1
< 0.1%
-322.4 1
< 0.1%
-316.79 1
< 0.1%
-314.55 1
< 0.1%
-305.6 1
< 0.1%
-301.325 1
< 0.1%
ValueCountFrequency (%)
395.5 1
< 0.1%
300.5 1
< 0.1%
291 1
< 0.1%
271 1
< 0.1%
251 1
< 0.1%
247.11 1
< 0.1%
171 1
< 0.1%
156.11 1
< 0.1%
151 1
< 0.1%
143.45 1
< 0.1%

D_BOPO
Real number (ℝ)

ZEROS 

Distinct7663
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70655963
Minimum-80518.28
Maximum80527.31
Zeros750
Zeros (%)3.0%
Negative12130
Negative (%)47.7%
Memory size397.0 KiB
2024-05-09T10:03:43.667781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-80518.28
5-th percentile-16.9665
Q1-2.56
median0
Q32.87
95-th percentile19.4295
Maximum80527.31
Range161045.59
Interquartile range (IQR)5.43

Descriptive statistics

Standard deviation743.69161
Coefficient of variation (CV)1052.5532
Kurtosis10839.64
Mean0.70655963
Median Absolute Deviation (MAD)2.7
Skewness-0.60405651
Sum17952.267
Variance553077.21
MonotonicityNot monotonic
2024-05-09T10:03:43.720027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 750
 
3.0%
0.5 38
 
0.1%
0.28 35
 
0.1%
-0.39 35
 
0.1%
0.39 34
 
0.1%
-0.5 34
 
0.1%
-0.47 33
 
0.1%
-0.81 32
 
0.1%
0.06 32
 
0.1%
0.7 32
 
0.1%
Other values (7653) 24353
95.8%
ValueCountFrequency (%)
-80518.28 1
< 0.1%
-18703.45 1
< 0.1%
-9109.53 1
< 0.1%
-8742.33 1
< 0.1%
-8699.45 1
< 0.1%
-7307.08 1
< 0.1%
-3796.47 1
< 0.1%
-2620.72 1
< 0.1%
-1821.652 1
< 0.1%
-1573.7 1
< 0.1%
ValueCountFrequency (%)
80527.31 1
< 0.1%
9109.33 1
< 0.1%
8496.45 1
< 0.1%
8096.25 1
< 0.1%
7300.7 1
< 0.1%
6359.49 1
< 0.1%
2995.77 1
< 0.1%
2910.69 1
< 0.1%
2482.98 1
< 0.1%
1881 1
< 0.1%

MA_BOPO
Real number (ℝ)

SKEWED 

Distinct13523
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.45659
Minimum-9159.275
Maximum40352
Zeros10
Zeros (%)< 0.1%
Negative12
Negative (%)< 0.1%
Memory size397.0 KiB
2024-05-09T10:03:43.776510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9159.275
5-th percentile62.66
Q178.25
median87.07
Q397.505
95-th percentile150.90125
Maximum40352
Range49511.275
Interquartile range (IQR)19.255

Descriptive statistics

Standard deviation398.38512
Coefficient of variation (CV)3.8883307
Kurtosis8260.1136
Mean102.45659
Median Absolute Deviation (MAD)9.46
Skewness83.134296
Sum2603217.1
Variance158710.7
MonotonicityNot monotonic
2024-05-09T10:03:43.830148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.01 80
 
0.3%
100 21
 
0.1%
266.3 20
 
0.1%
84.84 19
 
0.1%
99.46 16
 
0.1%
83.58 16
 
0.1%
82.73 15
 
0.1%
89.53 15
 
0.1%
338.06 14
 
0.1%
77.88 14
 
0.1%
Other values (13513) 25178
99.1%
ValueCountFrequency (%)
-9159.275 1
< 0.1%
-3276 1
< 0.1%
-3184 1
< 0.1%
-3092 1
< 0.1%
-2917 1
< 0.1%
-1193.765 1
< 0.1%
-305.73 1
< 0.1%
-140.11 1
< 0.1%
-100.115 1
< 0.1%
-90.965 1
< 0.1%
ValueCountFrequency (%)
40352 1
< 0.1%
40347.485 1
< 0.1%
8948.5 1
< 0.1%
8868.5 1
< 0.1%
8785.5 1
< 0.1%
8780 1
< 0.1%
8475 1
< 0.1%
4639.335 1
< 0.1%
4639.235 1
< 0.1%
4455.835 1
< 0.1%

D_CR
Real number (ℝ)

SKEWED  ZEROS 

Distinct10918
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1587233
Minimum-9960.68
Maximum17721.07
Zeros702
Zeros (%)2.8%
Negative12277
Negative (%)48.3%
Memory size397.0 KiB
2024-05-09T10:03:43.879057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9960.68
5-th percentile-18.1425
Q1-4.14
median0
Q34.05
95-th percentile17.9065
Maximum17721.07
Range27681.75
Interquartile range (IQR)8.19

Descriptive statistics

Standard deviation215.85961
Coefficient of variation (CV)186.2909
Kurtosis2522.2725
Mean1.1587233
Median Absolute Deviation (MAD)4.09
Skewness23.517006
Sum29440.841
Variance46595.37
MonotonicityNot monotonic
2024-05-09T10:03:43.927109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 702
 
2.8%
0.02 26
 
0.1%
-0.25 23
 
0.1%
-0.09 22
 
0.1%
-0.43 22
 
0.1%
-1.5 21
 
0.1%
-0.57 21
 
0.1%
0.61 21
 
0.1%
0.41 21
 
0.1%
0.43 20
 
0.1%
Other values (10908) 24509
96.5%
ValueCountFrequency (%)
-9960.68 1
< 0.1%
-8246.91 1
< 0.1%
-5928.24 1
< 0.1%
-5767.48 1
< 0.1%
-4702.96 1
< 0.1%
-4634.61 1
< 0.1%
-4055.25 1
< 0.1%
-3405.03 1
< 0.1%
-3228.56 1
< 0.1%
-2722.3 1
< 0.1%
ValueCountFrequency (%)
17721.07 1
< 0.1%
9828.32 1
< 0.1%
8260.73 1
< 0.1%
7763.34 1
< 0.1%
7325.23 1
< 0.1%
4484.32 1
< 0.1%
4264.88 1
< 0.1%
4058.58 1
< 0.1%
3338.7 1
< 0.1%
2824.56 1
< 0.1%

MA_CR
Real number (ℝ)

SKEWED 

Distinct13631
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.670631
Minimum0
Maximum11524.465
Zeros21
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size397.0 KiB
2024-05-09T10:03:43.977525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.01
Q113.755
median20.52
Q332.185
95-th percentile76.922
Maximum11524.465
Range11524.465
Interquartile range (IQR)18.43

Descriptive statistics

Standard deviation223.86416
Coefficient of variation (CV)5.3722286
Kurtosis807.73442
Mean41.670631
Median Absolute Deviation (MAD)8.145
Skewness25.357474
Sum1058767.4
Variance50115.16
MonotonicityNot monotonic
2024-05-09T10:03:44.030616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.74 80
 
0.3%
20.91 21
 
0.1%
0 21
 
0.1%
8.13 17
 
0.1%
17.07 17
 
0.1%
19.19 16
 
0.1%
11.6 15
 
0.1%
15.47 15
 
0.1%
27.28 14
 
0.1%
41.69 14
 
0.1%
Other values (13621) 25178
99.1%
ValueCountFrequency (%)
0 21
0.1%
0.1 1
 
< 0.1%
0.13 1
 
< 0.1%
0.2 1
 
< 0.1%
0.205 1
 
< 0.1%
0.22 1
 
< 0.1%
0.235 1
 
< 0.1%
0.26 1
 
< 0.1%
0.32 1
 
< 0.1%
0.405 1
 
< 0.1%
ValueCountFrequency (%)
11524.465 1
< 0.1%
8145.45 1
< 0.1%
7305.77 1
< 0.1%
7202.08 1
< 0.1%
7087.08 1
< 0.1%
7080.22 1
< 0.1%
6948.475 1
< 0.1%
6861.535 1
< 0.1%
6799.545 1
< 0.1%
6710.51 1
< 0.1%

D_NNPL
Real number (ℝ)

ZEROS 

Distinct6752
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1969116
Minimum-3000.88
Maximum3002.11
Zeros841
Zeros (%)3.3%
Negative12592
Negative (%)49.6%
Memory size397.0 KiB
2024-05-09T10:03:44.080411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3000.88
5-th percentile-4.91
Q1-0.94
median0
Q31.09
95-th percentile6.32
Maximum3002.11
Range6002.99
Interquartile range (IQR)2.03

Descriptive statistics

Standard deviation34.603101
Coefficient of variation (CV)175.72911
Kurtosis5104.2515
Mean0.1969116
Median Absolute Deviation (MAD)1
Skewness1.3778944
Sum5003.13
Variance1197.3746
MonotonicityNot monotonic
2024-05-09T10:03:44.132867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 841
 
3.3%
-0.25 96
 
0.4%
-0.08 85
 
0.3%
-0.04 78
 
0.3%
0.04 71
 
0.3%
-0.34 60
 
0.2%
0.5 59
 
0.2%
0.25 58
 
0.2%
-0.17 58
 
0.2%
0.17 57
 
0.2%
Other values (6742) 23945
94.2%
ValueCountFrequency (%)
-3000.88 1
< 0.1%
-1644.93 1
< 0.1%
-1155.02 1
< 0.1%
-1112.37 1
< 0.1%
-308.64 1
< 0.1%
-303 1
< 0.1%
-136.94 1
< 0.1%
-129 1
< 0.1%
-100 1
< 0.1%
-92.59 1
< 0.1%
ValueCountFrequency (%)
3002.11 1
< 0.1%
1541.93 1
< 0.1%
1538.74 1
< 0.1%
1150.37 1
< 0.1%
308.38 1
< 0.1%
163 1
< 0.1%
99.99 1
< 0.1%
85.45 1
< 0.1%
82.74 1
< 0.1%
82.45 1
< 0.1%

MA_NNPL
Real number (ℝ)

SKEWED  ZEROS 

Distinct9101
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9261793
Minimum-0.25
Maximum1580.5
Zeros314
Zeros (%)1.2%
Negative2
Negative (%)< 0.1%
Memory size397.0 KiB
2024-05-09T10:03:44.183740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.25
5-th percentile0.73
Q13.295
median6.625
Q312.55125
95-th percentile26.245
Maximum1580.5
Range1580.75
Interquartile range (IQR)9.25625

Descriptive statistics

Standard deviation27.689083
Coefficient of variation (CV)2.7895006
Kurtosis2120.9742
Mean9.9261793
Median Absolute Deviation (MAD)4.045
Skewness42.335588
Sum252204.36
Variance766.68533
MonotonicityNot monotonic
2024-05-09T10:03:44.238494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 314
 
1.2%
1 21
 
0.1%
2.5 21
 
0.1%
0.01 20
 
0.1%
2.96 20
 
0.1%
2.005 19
 
0.1%
3.17 18
 
0.1%
2.66 18
 
0.1%
1.86 18
 
0.1%
3.385 18
 
0.1%
Other values (9091) 24921
98.1%
ValueCountFrequency (%)
-0.25 2
 
< 0.1%
0 314
1.2%
0.005 1
 
< 0.1%
0.005 13
 
0.1%
0.005 1
 
< 0.1%
0.01 1
 
< 0.1%
0.01 20
 
0.1%
0.015 1
 
< 0.1%
0.015 7
 
< 0.1%
0.015 1
 
< 0.1%
ValueCountFrequency (%)
1580.5 1
< 0.1%
1528 1
< 0.1%
1527.56 1
< 0.1%
1526.945 1
< 0.1%
1407.5 1
< 0.1%
1256 1
< 0.1%
1191.5 1
< 0.1%
839.535 1
< 0.1%
788.035 1
< 0.1%
787.63 1
< 0.1%

CIU
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0.0
23248 
1.0
 
2160

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters76224
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23248
91.5%
1.0 2160
 
8.5%

Length

2024-05-09T10:03:44.288040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-09T10:03:44.327655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23248
91.5%
1.0 2160
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 48656
63.8%
. 25408
33.3%
1 2160
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 48656
63.8%
. 25408
33.3%
1 2160
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 48656
63.8%
. 25408
33.3%
1 2160
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 48656
63.8%
. 25408
33.3%
1 2160
 
2.8%

flag
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0.0
24491 
2.0
 
566
1.0
 
351

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters76224
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 24491
96.4%
2.0 566
 
2.2%
1.0 351
 
1.4%

Length

2024-05-09T10:03:44.366435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-09T10:03:44.401339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 24491
96.4%
2.0 566
 
2.2%
1.0 351
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 49899
65.5%
. 25408
33.3%
2 566
 
0.7%
1 351
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 49899
65.5%
. 25408
33.3%
2 566
 
0.7%
1 351
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 49899
65.5%
. 25408
33.3%
2 566
 
0.7%
1 351
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 49899
65.5%
. 25408
33.3%
2 566
 
0.7%
1 351
 
0.5%

class
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Normal
22818 
Siaga
 
1673
Awas
 
487
Waspada
 
430

Length

Max length7
Median length6
Mean length5.912744
Min length4

Characters and Unicode

Total characters150231
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 22818
89.8%
Siaga 1673
 
6.6%
Awas 487
 
1.9%
Waspada 430
 
1.7%

Length

2024-05-09T10:03:44.442299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-09T10:03:44.480344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
normal 22818
89.8%
siaga 1673
 
6.6%
awas 487
 
1.9%
waspada 430
 
1.7%

Most occurring characters

ValueCountFrequency (%)
a 27941
18.6%
N 22818
15.2%
o 22818
15.2%
r 22818
15.2%
m 22818
15.2%
l 22818
15.2%
S 1673
 
1.1%
i 1673
 
1.1%
g 1673
 
1.1%
s 917
 
0.6%
Other values (5) 2264
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 27941
18.6%
N 22818
15.2%
o 22818
15.2%
r 22818
15.2%
m 22818
15.2%
l 22818
15.2%
S 1673
 
1.1%
i 1673
 
1.1%
g 1673
 
1.1%
s 917
 
0.6%
Other values (5) 2264
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 27941
18.6%
N 22818
15.2%
o 22818
15.2%
r 22818
15.2%
m 22818
15.2%
l 22818
15.2%
S 1673
 
1.1%
i 1673
 
1.1%
g 1673
 
1.1%
s 917
 
0.6%
Other values (5) 2264
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 27941
18.6%
N 22818
15.2%
o 22818
15.2%
r 22818
15.2%
m 22818
15.2%
l 22818
15.2%
S 1673
 
1.1%
i 1673
 
1.1%
g 1673
 
1.1%
s 917
 
0.6%
Other values (5) 2264
 
1.5%

Interactions

2024-05-09T10:03:41.349628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-09T10:03:36.618557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:37.275023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:37.907341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:38.667334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:39.303589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:39.929209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:40.559639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:41.192683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:41.993713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:31.975177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:32.711869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:33.354451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:33.982769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:34.639176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:35.379593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:36.015941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:36.655908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:37.315118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:37.944326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:38.707559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:39.340014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:39.964644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:40.596743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:41.231485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:42.033309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:32.016355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:32.750710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:33.393793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:34.020678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:34.678967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:35.417842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:36.055515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:36.694742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:37.354979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:37.981624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:38.747884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:39.379516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:40.003415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:40.635562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:41.270513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:42.074565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:32.058625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:32.790053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:33.432564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:34.059446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:34.720508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:35.456057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:36.094273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:36.734658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:37.395541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:38.020551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:38.789050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:39.417300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:40.041497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:40.673983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:03:41.309050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-09T10:03:42.141243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-09T10:03:42.252936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PeriodeDataSandiBPRKPMMROABOPOCRNNPLD_KPMMMA_KPMMD_ROAMA_ROAD_BOPOMA_BOPOD_CRMA_CRD_NNPLMA_NNPLCIUflagclass
16532020-03-3160000144.341.0392.1480.996.410.3244.180-0.011.0350.3291.98067.4047.2900.386.2200.00.0Normal
32972020-06-3060000147.051.0092.1613.266.232.7145.695-0.031.0150.0292.150-67.7347.125-0.186.3200.00.0Normal
49522020-09-3060000145.832.0384.6714.265.81-1.2246.4401.031.515-7.4988.4151.0013.760-0.426.0200.00.0Normal
65982020-12-3160000144.760.8791.0617.324.95-1.0745.295-1.161.4506.3987.8653.0615.790-0.865.3800.00.0Normal
82472021-03-3160000144.570.6884.9417.474.64-0.1944.665-0.190.775-6.1288.0000.1517.395-0.314.7950.00.0Normal
98882021-06-3060000144.300.5885.4618.214.27-0.2744.435-0.100.6300.5285.2000.7417.840-0.374.4550.00.0Normal
115252021-09-3060000143.470.6690.4117.684.11-0.8343.8850.080.6204.9587.935-0.5317.945-0.164.1900.00.0Normal
131622021-12-3160000142.590.8590.6315.553.52-0.8843.0300.190.7550.2290.520-2.1316.615-0.593.8150.00.0Normal
147782022-03-3160000141.050.9090.2414.603.56-1.5441.8200.050.875-0.3990.435-0.9515.0750.043.5400.00.0Normal
163792022-06-3060000140.610.8790.4217.773.39-0.4440.830-0.030.8850.1890.3303.1716.185-0.173.4750.00.0Normal
PeriodeDataSandiBPRKPMMROABOPOCRNNPLD_KPMMMA_KPMMD_ROAMA_ROAD_BOPOMA_BOPOD_CRMA_CRD_NNPLMA_NNPLCIUflagclass
259082023-03-3162019124.452.8980.4118.234.321.6923.6050.212.785-1.2281.020-3.9120.1850.444.1000.00.0Normal
321922023-06-3062019120.072.8680.2522.504.71-4.3822.260-0.032.875-0.1680.3304.2720.3650.394.5150.00.0Normal
337822023-09-3062019119.832.5181.9325.124.54-0.2419.950-0.352.6851.6881.0902.6223.810-0.174.6250.00.0Normal
321962023-06-3062019229.520.6999.4311.7814.92-3.0331.0351.250.065-5.66102.260-1.2912.425-4.1817.0100.00.0Normal
337832023-09-3062019227.521.1095.0810.4812.96-2.0028.5200.410.895-4.3597.255-1.3011.130-1.9613.9400.00.0Normal
322002023-06-30620193219.234.8375.5052.540.00-100.79269.625-8.709.18013.8968.555-67.1486.1100.000.0000.00.0Normal
337842023-09-30620193181.753.3177.0227.490.00-37.48200.490-1.524.0701.5276.260-25.0540.0150.000.0000.00.0Normal
337852023-09-3062019427.713.0770.1911.714.201.3727.025-0.013.0753.5768.4052.4610.480-0.344.3700.00.0Normal
322082023-06-3062019525.910.4698.2537.537.820.7725.525-1.201.0603.8096.35028.1623.4505.145.2500.00.0Normal
337862023-09-3062019521.810.5697.8830.7115.79-4.1023.8600.100.510-0.3798.065-6.8234.1207.9711.8050.00.0Normal